Sea Level Rise: Faster than Projected

A new paper by Rahmstorf et al. compares observed climate changes, specifically global temperature and sea level rise, to projections from IPCC reports. The result: temperature is rising in outstanding agreement with IPCC projections, while sea level is rising faster than expected.

The new research doesn’t reveal any new data to supplant older observations. It simply compares data which are already freely available, to projections which were made years ago in previous reports from IPCC, namely the 3rd and 4th IPCC assessment reports.

When it comes to temperature, some of the computer models which form the basis of IPCC projections can realistically simulate factors like the el Nino southern oscillation (ENSO) which cause short-term fluctuations in temperature. But even though the events themselves can be realistically simulated, their timing doesn’t coincide with actual observed timing. It’s like correctly simulating how often it will rain, and how much — but not getting the actual dates of rainfall correct. The IPCC models also don’t include the volcanic eruptions which also cause fluctuations in temperature, or short-term variations in the energy output of the sun.

In short, they can’t be expected to get the short-term fluctuations right but can (we hope) correctly characterize their average influence. And that’s the best we can hope for. We don’t expect computer model simulations to predict the weather years, decades, or centuries in advance, but we do hope that they will correctly simulate what the average and variation of weather will be — which is the definition of climate.

Because the projections are the average of a great many model runs, they end up including the average influence of those fluctuations, but the fluctuations themselves are “smoothed out” by the averaging process. Hence actual observed temperature will show much larger year-to-year and even decade-to-decade variation than projected temperature, even if the models are performing correctly. In order properly to compare model projections to observed temperature change, we need to remove the influence of those short-term fluctuations from the observed data. That’s exactly what was done in Foster & Rahmstorf, so that analysis was updated through the end of 2011. It was also updated to use the new HadCRUT4 data set from the Hadley Centre rather than the older HadCRUT3v data set.

And when observed temperature, corrected for short-term fluctuations, is compared to IPCC projections, what’s the result? This:

The other process studied was sea level rise. We have over a century of global sea level data based on tide gauge measurements, and about 20 years of data from satellite altimetry. Both tide gauges and satellite data indicate that the present rate of sea level rise is about 3 mm/yr. But the IPCC models center around a projection of 2 mm/yr over recent decades. The comparison between the rates of sea level rise, projected and observed, looks like this:

Even the high end of IPCC projections for sea level rise rate fall short of what’s been observed. Simply put, sea level is rising faster than projected.

The IPCC projections don’t include dynamical processes which contribute to sea level rise, specifically the complexities inherent in the wasting of ice sheets. That’s why most researchers already considered the projections to be too conservative. The most recent report gave an upper limit to sea level rise by the year 2100 of about 59 cm, but if you were to poll those who are actively researching this issue you’d probably get a consensus nearer to 1 full meter increase by that time.

The sea level result garnered much more press than the temperature result. There are probably two reasons for this. First, the observed result really is faster than what was suggested by recent IPCC reports — and when observations are worse than expected, that’s news. Second, the recent devastation of the U.S. east coast caused by hurricane Sandy was made worse by the sea level rise we’ve already experienced, and that drives home the seriousness of possible future sea level rise. The issue of the danger caused by rising seas was already news.

Like it or not, sea level is rising and it’s probably going to be worse than the upper limit given in recent IPCC reports. Like it or not, it’s even possible that this century the oceans will rise even more than the not-so-conservative 1 meter many researchers expect. Like it or not — and nobody likes it — sea level rise has devastating consequences, not just for low-lying areas in third-world countries but for urban centers in the industrialized world. Let’s hope that our society has the wisdom and foresight to do something about it.

Mainly around Fox News where I am reliably told that Flynn’s theorem completely falls apart. I am a bit hazy on the details, but it something to do with anti-Flynn particles moving into a knowledge free vacuum.

Halldór Björnsson and deminthon might be surprised to discover that the bright folk here whom they seem to want to disparage are likely the cream of human intellect, and that David Benson is in fact correct.

To find out more, search for Gerald R Crabtree, and specifically for his recent papers in Trends in Genetics titled “Our fragile intellect. Part I” and “Our fragile intellect. Part II”. There is at least one word processor version kicking around if people don’t have institutional access, although that version omits the box comments in each of the submitted papers.

To assist, and because it more succinctly encapsulates the nonsense about the Flynn effect than I would have typed, here is the box from the second part:

Box 1. The Flynn effect

The famous Flynn effect, in which absolute IQ scores increased during the first 50 years after the institution of these tests, seems at first glance to contradict the hypothesis that we are losing our intellectual abilities. However, these changes in IQ scores are probably linked to environmental influences including reduction in lead and other heavy metals used in gasoline and paint and the virtual elimination of hypothyroidism in children due to the widespread use of iodinated salt. These and many other advances in prenatal care and prevention of anoxia during childbirth have clear effects on our average intellectual abilities. In addition, scores on these tests have been shown to correlate well with preschooling and other societal influences instituted during the period over which test scores were compared. Consistent with these hypotheses, the gains recorded are predominantly in the raising of lower scores. However, since about 1985 or 1990 these absolute IQ scores have been dropping in some studies, despite considerable ‘teaching to the test’ as well as the general awareness among children that it is important to score well on the test. Most likely these short-term effects are not genetic because the genetic effects at issue are only likely to operate over hundreds of years, not decades. In addition, Flynn points out that we are not getting more intelligent, but instead we are getting ‘smarter’ at taking the tests because our everyday experiences are becoming more like the tests. This is similar to the way that practicing tennis makes one a better badminton player.

Interestingly Crabtree believes that current human ingenuity will help to circumvent any future eroding on the intellectual genome. I think that he is making several leaps of faith in doing so, including omitting the very probable loss of much technology post-Peak Oil, post global environmental collapse, and post-global politico-economic chaos. Given that current younger generations are rapidly losing practical skills as they rely heavily on the complexities of 21st century information technology and energy-intensive machinery, any crash of this complex system is going to seriously disrupt the intellectual capital invested in this particular cultural manifestation of human intellect.

I’d dearly love to see the progress of humans ten thousand years hence, given the way in which the skittles have been set up today…

“…Crabtree believes that current human ingenuity will help to circumvent any future eroding on the intellectual genome. I think that he is making several leaps of faith in doing so, including omitting the very probable loss of much technology post-Peak Oil, post global environmental collapse, and post-global politico-economic chaos.”

If that is correct–and I sure wouldn’t say it isn’t, at this point–then classic Darwinian selection for intelligence comes back into play, presumably. So I’m not worried about genetically-based declines in intelligence.

Cultural capital is quite another matter. A former colleague of mine observed that his music students didn’t, by and large, grasp what a privilege it was to sit around in a comfortable classroom discussing Mozart (as opposed to what their grandparents had to do, by and large, which was work their butts off doing the agricultural ‘heavy lifting.’)

Indeed! Thanks Tamino, Stefan and Anny! This work only further highlights some of the previous work on the storm surge risk along the east coast, among the myriad of other issues related to sea level rise.

Great post, Tamino. But there is a typo here–should say “projected”: “Hence actual observed temperature will show much larger year-to-year and even decade-to-decade variation than observed temperature”

I found the paragraph interesting proposing sea level rise accelerates with temperature rise. Taking the still not excludable +4°C scenario at the end of this century would then result in a SLR of 15mm per year.

Isn’t that already a dimension which might be easily translated into people and jobs forced to move at higher ground per year?

Well, if you look at ancient records in ice-cores, fluctuations can become so extreme that completely different climate regimes occur within a few years, yes, less then 5 years. See e.g. R.B. Alley ‘Ice-core evidence of abrupt climate changes’.

[Response: As far as I’m aware, such rapid abrupt changes (like Dansgaard-Oeschger events) are regional rather than global, while large paleo changes in global average temperature take much longer.]

Agreed Tamino, But that is the point I was getting at. Just how extreme are the local and short term fluctuations. For example; How soon could an urban area get a few days of 150 degree temperatures?

[Response: I think that Greenland ice core data indicate extreme shifts within a decade or less. 150 deg.F in NYC seems more extreme than I recall, but 120F is conceivable. Or did you mean 150C? (just kidding)]

From what I understand there is not much we can do about sea levels, as significant increases are locked in for the next few hundred years. But at least we can plan future development with increasing sea levels in mind, and not waste too much effort defending that which will be lost anyway.

In one of the Swanson and Tsonis papers they discuss a regime shift characterized by anomalously high rates of warming in the deep ocean, and essentially a flattish surface temperature trend for several decades.

When you say “it’s probably going to be worse than the upper limit given in recent IPCC reports”, are you including AR4, which says “this report does not assess the likelihood, nor provide a best estimate or an upper bound for sea level rise”?

Here’s a rainy day file candidate, tamino: http://www.earth-syst-dynam-discuss.net/3/561/2012/esdd-3-561-2012.html. It was thrown at me in a comment stream. It was in a list that included such luminaries as Chilingar, Humlum, and He Who Shall Not Be Named (to avoid keyboard soaking) . . . ok, I’ll name him – Tim Curtin. I don’t have the statistical pork chops (not even the stuffing) to question the methodology.

I too have no idea DSL, except that it mentioned taking differences, not once, but twice. When you have noisy data, taking differences gives you something that tends to look pretty random. Do it again, and I suspect you could prove that black was white.

To be more specific: when you do tests like this on first differences or second differences, you’re looking for correlations between the tiny little short-term fluctuations between datasets: the big long-term changes have been squeezed out of the data. So what Beenstock et. al. has proven is that CO2 isn’t responsible for those fluctuations. Big deal: we’ve known that all along. The problem is that those who cite Beenstock don’t realize that, and can’t see the forest for the trees.

It’s simply amazing how frequently statistics-manipulating denialists of human-caused climate change use differentiation to Thimblerig their conclusion. I am not at all surprised to hear that Tim Curtin was riding on this train – he’s a repeat offender of this blatant lying-through-incorrect-mathematics approach.

DL, where is the comments stream to which you referred at the top of this branch?

They told him don’t you ever blog around here
Don’t wanna see your crap, you better disappear
The stats in their posts and their words are really clear
So dif it, just dif it

You better “dif,” you better do what you can
Don’t wanna see no blood, don’t be a Wattshow man (uh)
You wanna be smart, better do what you can
So dif it, but you wanna be bad

Just dif it (dif it) dif it (dif it)
No one wants to be difeated
Showin’ how funky strong it’s your fight
It doesn’t matter who’s wrong or right
Just dif it, dif it
Just dif it, dif it
Just dif it, dif it
Just dif it, dif it

Well, yes, I know it’s a parody. It probably says more about me that “Eat It” comes to mind before “Beat It”…. much more entertaining (to me)… especially when talking about fake skeptic’s “science”, which is a parody unto itself…

Atmospheric temperature is not going to rise very rapidly as long as there is some ice at each pole.

Sea level rise is a function of the amount of heat absorbed. The community general circulation models underestimate how much heat is being accumulated on a global basis. Transfer of heat from the atmosphere to ice makes that heat disappear in the HadCRUT4 data set. As long as there is ice and wind, atmospheric temperature data does not tell us how much heat has been absorbed by the climate system.

Heat absorption has just kinked upward as the Arctic albedo plunges.

Some of that extra heat is driving carbon feedbacks (melting permafrost). (Some of the permafrost melt is also going into sea level rise.) These carbon feedbacks will turn modest model errors into large errors. At this point, it is no longer rational to disregard carbon feedbacks as part of the expected greenhouse gases in the atmosphere.

The formation of moulins on the GIS tells me that large volumes of ice are warming and weakening throughout their volume. When big ice warms and weakens, it collapses under its own weight. The potential energy of the elevated mass supplies the energy required to drive the breakup of the ice structure. This results in rapid sea events as significant volumes of ice sheets progressively fracture and flow as a water/ice slurry at significant horizontal speeds. The only reasonable estimate of sea level rise is a few meters in a several decades. Given the impacts of storm tides in a time of storms of increasing power, a better estimate is not really required.

Tamino , do you compare the temperature curve with the entire range of scenarios used in the IPCC report? if the goal is to assess the climate sensitivity, wouldn’t it be more sensible to compare it only with the actual emission curves ?

I gave up reading about half way through. It’s a bit long, and he doesn’t elucidate his model very precisely – I think it’s some kind of nonparametric bootstrapped AR(1) or AR(2) model, but it wouldn’t surprise me if I’ve got the wrong end of the stick.

The problem here is that the author of that (Matt Asher) uses a non-independent random walk i.e., each year’s random jump starts where the previous year ended, instead of starting at the mean. Those kind of random walks tend to run off wildly in one direction or another. In the real world, the random yearly temperature fluctuations are caused by dynamic instability in parts of the global climate system. These instabilities cause the global temp to overshoot or undershoot the true mean global temp, which is constrained by Conservation of Energy. That real-world constraint means that the global temp will eventually come back to where it “belongs”. In other words, the non-independent random walk does not, and cannot, model the real world.

If you must use a random walk to model global yearly temps, you have to use an independent walk instead: each year’s jump starts at the mean, rather than at last year’s result. If you do that, you will find it’s impossible to model the global temperature since 1881 without a trend. Which nicely proves what Asher believes he has disproven.

Formerly known as “random walk” but after being repeatedly misused by denskepticogs is now called “blind staggers,” symptomatically similar to pathology of selenium or ergot poisoning yet purely psychological in nature.

He asks, IF temperature is a random walk, what is the probability that we would see the temperature trend we do see. He then shows that under the assumption that the time series is a random walk, that what we see is not exceptional.

Temperature is a physically constrained parameter. It is a function of the energy in and out of the system. Because it is physically constrained, one expects to see ‘regression to the mean.’ An extreme value should be followed by subsequent values returning toward the physically-constrained mean value. His random-walk model requires him to assume that there is no tendency to a regression to the mean, meaning that there are no relevant physical constraints – which is as physically unrealistic an assumption as I can imagine anyone making.

He attempts to address this with his ‘autocorrelation’ series of analyses. What he misses, though, is that for a physically constrained parameter, the tendency toward regression to the mean becomes stronger, the more extreme the value. ie, if the average August high temperature in say, Chico, CA, is 90F, and the observed temperature is 115F, there is a MUCH stronger probability of declining temperature (regression to the mean) over the next several days than if the observed temperature is 92F. He applies 2 year and 3 year trends – but always from the mean, not from values far off the mean, because he assumes for every iteration of his random walk (every year) that the value he starts with IS the mean.

A random walk requires an assumption of no regression to the mean – no stabilizing influence tending to return temperatures to a constrained value. His autocorrelation results show that there is regression to the mean – hell, he SAYS that this implies physical constraints to the possible temperature values, which inherently invalidates his entire ‘random walk’ null hypothesis. t is a fundamentally invalid analysis, violating its own untested assumptions.

Fa[c]t free physics? Is that like diet physics? Zero calorie physics? [Cold fusion fits there, I think.] Physics Lite? Looks and tastes like physics, with none of the facts! Can you tell the difference? WUWT can’t!

Bob, Matt Asher’s post is classic mathturbation. It is surprising that someone with even an undergraduate education in stats would not realize that applying a totally inappropriate model to a physical system would yield garbage results. I don’t know whether Matt is just stupid or deluded or a cynical manipulator.

In it you state that: “One pertinent case occurs when the two (or more) of the predictor variables are very strongly correlated. For instance, if we regress global temperature as a function of time, and of the logarithm of population, then we find that those two predictors are strongly correlated. Since both of them are increasing (in similar fashion), will the global temperature increase be due to one or the other (or both or neither) factor? In such cases, the matrix has a determinant which is close to zero, which makes it “ill-conditioned” so the matrix can’t be inverted with as much precision as we’d like, there’s uncomfortably large variance in the final parameter estimates”.

This sounds a lot like the spurious regressions one gets when regresing two non stationary series? Is this the same effect, if so can ridge regression be used to regress non-stationary data on each other?

Also you mention that you would expect to have several sets of observations for both the y’s and the x’s possibly at different times (but not necessarily).

I have a situation where I want to fit four basis equations with four weights (Betas) which are fitted to 50 observations all made at one instant in time.

Now I know that I could do regular OLS regression in this instance and from your description here it seems ridge regression would be fine too.

But you also go on to show how ridge regression is similar to principal components. In this instance I persume you are applying the PCA to a covariance matrix of the expantory variables. However in my case as I only have one set of observations of each varaible. So I can’t really produce a meaningful correlation matrix? Does this simply mean that PCA (as you show it in you article) and ridge regresion are only equivalent when you have multiple observations, i.e. in my case the same 50 points observed on successive dates or can PCA be used in my case too?

Lastly there is another option involving PCA, in my case I have 4 regressors fo 50 unknowns so my x matrix is not square. But I can use sigular Value decomposition to run the regression as shown in this paper:

Calling sustainability journalists and advocates everywhere to investigate the ‘no man’s land’ of human population dynamics. A cascade of ecological events with unforeseen consequences is occurring around us in our planetary home. There are multiple causes. But human overpopulation of Earth is the prime factor.

Climate scientists are speaking out. Where are the population scientists? Why are they not more vocal?

The deliberate silence among population scientists with unfulfilled responsibilities to assume and duties to perform with regard to their skillful examination and careful reporting of extant research on “human population dynamics” cannot be excused by the recognition that such woefully inadequate behavior “exists in all professions”. There is much too much at stake. Scientists have to stand up and consciously speak out about what is true to them, according the ‘lights’ and scientific knowledge they possess.

Solzhenitsyn reported, “One word of truth overcomes the world.” Could it be that for the lack of one word, one word by people in possession of truth, as their lights and science indicate ‘what is’, the world and life as we know it is being destroyed before our eyes? As the sages of old said, perhaps it is time, finally, now and here to “speak the truth as if you are a million voices, for your silence is killing the world.”

[Response: This blog is about global warming, not population dynamics.]

A global warming blog that prohibits any discussion of population is like a math blog that prohibits any discussion of statistics. It is improbable..

So long, and thanks for all the fish!

[Response: Spare us your stupid horseshit.

If I prohibited talk of population I wouldn’t have allowed the comment at all. But clearly the commenter wants to focus on that issue. That’s fine. He has his own blog. I even allowed him to plug it here.

But threads are often hijacked by those who want to push another agenda (ask the RealClimate people about nuclear power). His comment wasn’t about global warming, or climate science, and it sure as shit wasn’t about sea level rise — the topic of this post. I gave him his one self-promotion, but I also warned him that this blog is not about that, so he’d know that attempts to make it so aren’t gonna work.

Aaron, I don’t think you’re seeing this clearly. The guy doesn’t want to discuss “population dynamics”, but rather that there are too many “other” people on this planet, and somebody oughtta do something about that (wink wink).
I’m with tamino here. You don’t want to go there.

I’m absolutely with Tamino and Gavin’s Pussycat on this, and my PhD was focussed on life histories and population dynamics.

Responding to global warming is an issue that really needs to start at a place quite separate to that of population. Warming is a “now” problem, an urgent problem, and one that will have long bolted from the barn before any usefulness is achieved by even extreme controlled attempts to massage demographic trends. Short of catastrophic stochastic events, it’s just how population trajectories work.

This is not to say that population is not important (I’m on record many times pointing at this elephant in the room). However, in terms of climate change response population is of secondary importance. There’s also an ethical issue – it’s a bit rich for rich First World countries to put the population screws on the Third World in order to carry the responsibility for the damage mostly incurred by the First World.

Yes, the population problem needs to be addressed, If we don’t address it soon, the laws of nature will do it for us and in a red-in-tooth-and-claw manner, through some combination of the the three Horsemen that are war, famine, and disease. Don’t however think that the answer to human-caused climate change lies in population manipulation – that’s simply a self-delusion that will not address the root cause of the impact we’re inflicting on the biosphere.

Of course, if you’re thinking of advocating for global nuclear warfare or pandemic lethal disease – in pre-emption of what is likely coming anyway – then you might actually be talking about something that will make a difference to the climate, but I doubt that you’ll get that past any ethics committee, government, or electorate.

If we are to have any hope of solving our fossil-fuelled mess at this eleventh hour and fifty-ninth minute (plus four and a half dozen seconds), it has to be done with appropriate prioritisation. Pointing at another issue, albeit related but not the immediate root problem, will only distract and delay even further the action that is so urgently required.

And for the record there have been extended discussions on population here. I’m pleased that they’re on the record, because they are relevant. But as Tamino says this is a climate change blog, and it’s important not to be distracted by what is a side issue to the theme.

Dear All
How much effect does the melting of ice sheets, warming of oceans etc have on reducing the atmospheric temperature rise? Could the recent flat temperatures (despite a planetary radiative imbalance) be caused by the simultaneous ice melt? Thanks.

I did a brief calculation a day or two ago and think it came out to about (for Greenland and Antarctica) 1% of the annual heat accumulation, toward latent heat to melt ice. It’s not likely ice has at all abated the warming.

Hi Alex
Thanks for this. So it seems that melting of ice, ocean warming etc doesn’t have enough influence on the radiation budget to affect surface temperature. For me that’s a surprising result, but I haven’t done the math so I can’t say either way. Do you know of any papers which develop this idea?

I have not been able to read the paywalled article but there is a lot of debate about it. Authors suggest original projections have proven to be accurate but warming is 0.39º/20yrs, projected figure is 1.1º/40yrs, so many disagree. I’m guessing if the warming is not linear – 0.39 in the first half, and 0.62 in the back half is about right….?

> but … so many disagree
Nope. The abstract says something different than you describe:

“. It contained a prediction of the global mean temperature trend over the 1990–2030 period that, halfway through that period, seems accurate. This is all the more remarkable in hindsight, considering that a number of important external forcings were not included. So how did this success arise? In the end, the greenhouse-gas-induced warming is largely overwhelming the other forcings, which are only of secondary importance on the 20-year timescale.

There seems to be a contradiction between Rahmstorf, et. al. and Gregory, White , Church, et. al. (2012) Twentieth-century global-mean sea-level rise: is the whole greater than the sum of the parts. Both sets of authors are highly regarded within the sea-level rise community.